The Model Related to Hypoxia and Endoplasmic Reticulum Stress Predicting Prognosis and Immunotherapy Response in Uterine Corpus Endometrial Cancer.

Hypoxia and endoplasmic reticulum stress (ERS) response play key roles in the microenvironment and disease progression of tumors, tightly linked with the survival prognosis of patients. However, the prognostic value of hypoxia and ERS-related genes (HERs) in uterine corpus endometrial cancer (UCEC) remains to be illuminated. The training and testing sets used included clinical data from UCEC patients obtained from the Cancer Genome Atlas database. The univariate Cox regression analysis was first carried out on tumor samples of the training set. Subsequently, protein-protein interaction (PPI) networks and LASSO regression were applied to narrow down the range of key genes. Finally, feature HERs were screened out through multivariate Cox regression analysis, with a risk model constructed. The model’s predictive ability was examined through receiver operating characteristic (ROC) curves. UCEC patients were further clustered into high-risk (HR) and low-risk (LR) groups according to riskscores, which were then subjected to enrichment analyses (GO and KEGG analyses, immune cell infiltration, gene mutation analysis, and drug sensitivity analysis. 9 HERs were identified in UCEC, and a risk model was generated. The ROC curves manifested that the model exhibited good predictive ability (Area Under Curve (AUC) > 0.68). UCEC patients were clustered into HR and LR groups based on the median riskscore. The K-M curve indicated better survival rates for patients in the LR group. The differentially expressed genes (DEGs) between the two groups were majorly enriched in functions such as receptor-ligand activity, passive transmembrane transport activity, monatomic ion channel activity, as well as pathways such as neuroactive ligand-receptor mediation, Cytokine-cytokine receptor interaction, and Calcium signaling pathway. Immune infiltration analysis demonstrated that Tregs, CD8+ T cells, Neutrophils, and T helper cells were highly infiltrated in the LR group (P < 0.001). The mutation analysis indicated that the gene mutation rate of the model gene in the LR group was higher than that in the HR group (19.52% vs. 15.19%). Based on the drug screening results, IDH-C227, P-529, Okadaic acid, AM-5992, and Telatinib were promising drugs for treating UCEC. In this project, we created a UCEC prognostic model based on HERs, preliminary figuring out immune cell infiltration at different risk levels and identifying several potential anti-tumor therapeutic drugs. These findings are expected to proffer value for in-depth research on UCEC, including disease progression, prognosis evaluation, immune response, and drug screening.